Publications by authors named "Zuyi Yu"
Article Synopsis
- The paper addresses the challenge of accurately identifying the location and electrical activity of brain sources in epilepsy using EEG recordings due to the complexity of the problem.
- A new approach using simulation-driven deep learning is proposed, which incorporates a patient-specific model trained on high-resolution EEG simulations and utilizes neural networks to analyze spatial and temporal features.
- The performance of this method shows significant improvements in dipole localization accuracy compared to existing deep learning and classical techniques, tested on both synthetic and real EEG data from patients with drug-resistant epilepsy.
Epilepsy is one of the most common neurological diseases, which can seriously affect the patient's psychological well-being and quality of life. An accurate and reliable seizure prediction system can generate alarm before epileptic seizures to provide patients and their caregivers with sufficient time to take appropriate action. This study proposes an efficient seizure prediction system based on deep learning in order to anticipate the onset of the seizure as early as possible.
View Article and Find Full Text PDFThe automatic seizure detection system can effectively help doctors to monitor and diagnose epilepsy thus reducing their workload. Many outstanding studies have given good results in the two-class seizure detection problems, but most of them are based on hand-wrought feature extraction. This study proposes an end-to-end automatic seizure detection system based on deep learning, which does not require heavy preprocessing on the EEG data or feature engineering.
View Article and Find Full Text PDFAutomatic seizure detection based on kernel robust probabilistic collaborative representation.
Med Biol Eng Comput
January 2019
Visual inspection of electroencephalogram (EEG) recordings for epilepsy diagnosis is very time-consuming. Therefore, much research is devoted to developing a computer-assisted diagnostic system to relieve the workload of neurologists. In this study, a kernel version of the robust probabilistic collaborative representation-based classifier (R-ProCRC) is proposed for the detection of epileptic EEG signals.
View Article and Find Full Text PDFBackground: The purpose of this work is to benchmark RapidPlan against clinical plans for liver Intensity-modulated radiotherapy (IMRT) treatment of patients with special anatomical characteristics, and to investigate the prediction capability of the general model (Model-G) versus our specific model (Model-S).
Methods: A library consisting of 60 liver cancer patients with IMRT planning was used to set up two models (Model-S, Model-G), using the RapidPlan knowledge-based planning system. Model-S consisted of 30 patients with special anatomical characteristics where the distance from planning target volume (PTV) to the right kidney was less than three centimeters and Model-G was configurated using all 60 patients in this library.